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Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.03
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Abstract

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.02

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Date: 22. 8.2014 17:05:18

Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.02

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Date: 13. 9.2014 14:45:22

Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.02

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Date: 20. 1.2015 18:30:22
18. 9.2018 18:22:56

Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.01

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Date: 11. 6.2012 14:22:34

Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.01

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Date: 22. 3.2013 19:34:49

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